A foundational model for in vitro fertilization trained on 18 million time-lapse images

Suraj Rajendran, Eeshaan Rehani, William Phu, Qiansheng Zhan, Jonas E. Malmsten, Marcos Meseguer, Kathleen A. Miller, Zev Rosenwaks, Olivier Elemento, Nikica Zaninovic and Iman Hajirasouliha ()
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Suraj Rajendran: Weill Cornell Medicine of Cornell University
Eeshaan Rehani: Weill Cornell Medicine of Cornell University
William Phu: Weill Cornell Medicine of Cornell University
Qiansheng Zhan: Weill Cornell Medicine
Jonas E. Malmsten: Weill Cornell Medicine
Marcos Meseguer: IVIRMA Valencia
Kathleen A. Miller: IVF Florida Reproductive Associates
Zev Rosenwaks: Weill Cornell Medicine
Olivier Elemento: Weill Cornell Medicine of Cornell University
Nikica Zaninovic: Weill Cornell Medicine
Iman Hajirasouliha: Weill Cornell Medicine of Cornell University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Embryo assessment in in vitro fertilization (IVF) involves multiple tasks—including ploidy prediction, quality scoring, component segmentation, embryo identification, and timing of developmental milestones. Existing methods address these tasks individually, leading to inefficiencies due to high costs and lack of standardization. Here, we introduce FEMI (Foundational IVF Model for Imaging), a foundation model trained on approximately 18 million time-lapse embryo images. We evaluate FEMI on ploidy prediction, blastocyst quality scoring, embryo component segmentation, embryo witnessing, blastulation time prediction, and stage prediction. FEMI attains area under the receiver operating characteristic (AUROC) > 0.75 for ploidy prediction using only image data—significantly outpacing benchmark models. It has higher accuracy than both traditional and deep-learning approaches for overall blastocyst quality and its subcomponents. Moreover, FEMI has strong performance in embryo witnessing, blastulation-time, and stage prediction. Our results demonstrate that FEMI can leverage large-scale, unlabelled data to improve predictive accuracy in several embryology-related tasks in IVF.

Date: 2025
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DOI: 10.1038/s41467-025-61116-2

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